Exploring PolSAR Images Representation via Self-Supervised Learning and Its Application on Few-Shot Classification

Deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) image classification over the past few years. However, for supervised learning (SL)-based methods, it is quite difficult to get large amounts of high-quality and labeled PolSAR data in real applications. In addition, there is a problem of poor generalization for the method of specific supervision labels. To solve the abovementioned issue, we explore how to learn representations from unlabeled data from a new perspective. In this letter, a self-supervised PolSAR representation learning (SSPRL) method is proposed. Different from the SL-based methods, SSPRL aims to learn PolSAR image representations via an unsupervised learning approach. Specifically, a self-SL (SSL) method without negative samples is explored, and a positive sample generation (PSG) approach and a novel encoder architecture designed for PolSAR images are proposed. Moreover, mix-up is implemented as a regularization strategy. Furthermore, the convolutional encoder is utilized to transfer the feature representation from the unlabeled PolSAR data to the downstream task, that is, to achieve the few-shot PolSAR classification. Comparative experimental results on two widely used PolSAR benchmark datasets verify the effectiveness of the proposed method and demonstrate that the SSPRL produces impressive performance on few-shot classification task compared with state-of-the-art algorithms.